Accurate Coding: The Foundation Of Accountable Care - Optum
Accurate coding:the foundation of accountable careJeremy Orr MD, MPH, Chief Medical Officer, Optum AnalyticsAllen Kamer, Chief Commercial Officer, Optum AnalyticsWhite PaperOptumwww.optum.comPage 1
White PaperAccurate coding: the foundation of accountable careThe problem with patients who have conditions that go unrecognized,or “uncoded patients” is not a new one. It has been a problem foryears and providers have known they need to tackle it, to improve bothpatient care and financial viability. Yet the problem persists. To calculate its importance, a team at Humedica quantified the rate of uncodedpatients, and then analyzed its impact on utilization and outcomes. Inshort, the findings indicate that better coding identifies many high-riskpatients and ultimately improves their care to drive better outcomes.Thirty-seven percent of patients with major chronic conditionsgo uncodedThe research indicates that 37% of patients with major chronic conditions are uncoded. To arrive at this result, data was mined from over 4 million active patients withevidence of major chronic diseases. Humedica’s aggregated data identifies uncodedpatients based on clinical evidence such as lab results, provider notes and medicationsprescribed. This allows patients to be found despite the lack of a coded diagnosis on aclaim, or an entry on an electronic health record (EHR) problem list.Based on the analysis, 37% of patients with evidence of diabetes, congestive heartfailure, coronary artery disease, hypertension, or dyslipidemia are uncoded for one ormore of these conditions. Put another way, about 17% of all patients in a typical medical group are uncoded for at least one major chronic condition. For a medical practiceof 500,000 patients, this translates to about 83,000 patients who are uncoded for oneor more of these major chronic conditions.Typical provider group has significantuncoded patient populationEXAMPLE: In a typical 500K patient practice 214%42.542 59%19.54%14028%Uncoded ppatients*Uncoded patients* NoDXcodeandnotonproblem list- No DX code and not onproblemlist Evidenceof condition via RX, lab, ejection fraction- Evidence of condition via lab,Resultsinfractionsubpar outcomesRX,ejection- Resultsin subparoutcomesrevenueResultsin missed- Results in missed revenue27755%37% of37%ai major withpatients3patients% offwithihchronic condition are uncodeda majorjchronic condition are uncodedPatients without evidence of 1 major chronic conditionFully-coded for all conditions with evidenceUncoded, RX evidenceUncoded, other evidence*Uncoded, Rx other evidenceNote: Only accounts for patients with evidence of DM, CHF, CAD, HTN, DYS. An ‘uncoded’ patient is not coded for 1 or more of0Note:accounts‘Other’for patientswith evidenceDM, CHF,CAD,onHTN,An ‘uncoded’is not patientscoded forin 5 chronic conditiontheseOnlyconditions.evidence clinicalofresults.BasedpreDYS.go-livedata frompatient4M active1cohorts,or more fromof these‘Other’Prevalenceevidence clinicalresults.Based onbasedpre go-livedata from4M active patients26 conditions.medical groups.of chronicconditionson currentstate.in 5 chronic condition cohorts, from 26 medical groups. Prevalence of chronic conditions based on current state.Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.0Figure 1: Typical provider group has significant uncoded patient populationOptumwww.optum.comPage 2
White PaperAccurate coding: the foundation of accountable careThe problem persists across conditionsPoor coding is a significant problem for each of the major chronic conditions analyzed.Based on the data, the incidence of uncoded patients for diabetes, chronic heart failureand hypertension is 22%. For dyslipidemia, 28% of patients are uncoded. And finally,12%of patientswithofcoronaryartery diseaselack codes. acrossHighratesuncodedpatientschronic conditions% of Patients uncoded by major chronic sCHFHTNDYSCADNote: DM N 1.4M, CHF N 240K, HTN N 2.7M, DYS N 2.7M, CAD N 620K. Based on preNote:DM Ndata 1.4M,N active 240K,patientsHTN N in2.7M,DYS 2.7M, CADN 620K.Basedon prego-live dataC4M SNCgo-livefromCHF5 chronicconditioncohorts,from 26medicalgroups.from 4M active patients in 5 chronic condition cohorts, from 26 medical groups.1Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.Figure 2: High rates of uncoded patients across chronic conditions1The relatively low rate of uncoded coronary artery disease patients makes sense giventhis diagnosis requires a major event. On the other hand, the diagnosis of dyslipidemia,based primarily on lab results, is missed more often. The fact that uncoded rates arehigh across conditions points to a significant opportunity for clinical analytics. Robust,condition-specific clinical algorithms for each condition can increase diagnosis ratesdramatically.Uncoded patients are relatively poorly controlled, many with comorbiditiesBefore analyzing the impact coding can have on utilization and outcomes, there wasa need to understand uncoded patients in more detail. At baseline, uncoded patientstend to be relatively sick. Thirty percent of uncoded patients have evidence of twomajor chronic conditions. An added 13% have evidence of three conditions. In addition,analysis indicates many of these patients are poorly controlled.This analysis focused on conditions that are responsive to ambulatory care interventions,specifically diabetes, hypertension and dyslipidemia. A scoring system was developedto estimate each patient’s risk level at baseline. For each condition, relevant clinicalmeasures and their respective risk thresholds were defined. For each patient, risk levelwas calculated based on how many clinical measures relevant to him or her were atrisk. Patients defined as “very high risk” had more than 75% of their relevant clinicalmeasures at risk. “High risk” patients had between 50 and 75% of relevant measures atrisk. “Moderate risk” patients had between 25 and 50% at risk.Optumwww.optum.comPage 3
White PaperAccurate coding: the foundation of accountable careMeasureAt RiskA1C 9LDL 130BPSYS 140 & DIAS 90HDL 40TRIG 200Figure 3: Risk thresholds by clinical measureBased on this methodology, 36% of uncoded patients were at-risk. Eleven percent ofthem fell into the highest risk category versus only 6% of coded patients. This meansa significant number of uncoded patients are relatively complex. They have high risk offuture problems and need higher levels of care. However, these patients access significantly less ambulatory care than coded (p-value .0001).Uncoded patients tend to be more at-risk% of Patients at-risk*12 months before coded15%10%Very high riskHigh riskModerate risk5%0%Note:DataDataforfor561Kpatientsrisk patientshave thanmore75%thanof relNote:561KppatientswithwithDM,,DM,HTN,HTN,, DYS.DYS.VeryyVeryhighg highrisk ppatientshave more% of 75%relevantclinicalevant clinicalmeasuresrisk.51-75%High riskhave 51-75%of relevantmeasuresriskat risk.havemeasuresmeasuresat risk.High riskathaveof relevantmeasuresat risk. ModeratehaveModerate26-50% of topatientslive24Months onpopulationanalytics.at risk. Limited to patients live 24 Months on MinedShare.2Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.2Figure 4: Uncoded patients tend to be more at-riskUncoded use less ambulatory care, more acute careDespite relatively high levels of risk, uncoded patients access much less ambulatory carethan coded patients. Their rate of primary care utilization is 42% lower. Total outpatientvisits are 31% lower. For those visits that do occur they are billed at lower rates, as therate of level 4 and 5 visits is 43% lower.Optumwww.optum.comPage 4
White PaperAccurate coding: the foundation of accountable careUncoded patients access less outpatientcare more acute carecare,Uncoded vs. coded patient utilization*Comparison 12 months before ‘go-live’Uncoded CodedAAcutet care utilizationtili tiUncoded Coded14%ER Visits19%Inpatients Visits17%Length of Stay/inpatient visitAmbulatory care utilization-42%PCP Visits-31%Outpatient Visits (All)Level 4 & 5 ote: Data based on utilization prior to go-live for 561K active patients in 3 chronic condition cohorts (HTN, DYS, DM).Comparesaveragerates forcodedversus uncodedpatientsthe 12 monthsbefore go-liveon MinedShare.LimitedNote: Databasedutilizationon utilizationpriorto go-livefor 561Kactiveinpatientsin 3 chronicconditioncohorts (HTN,topatients24 monthson zationrates .0001.for coded versus uncoded patients in the 12 months before goproperty of Optum. Do not distribute or reproduce without express permission from Optum.live on population analytics. Limited to patients liveConfidential24 monthson population analytics. P-value .0001.3Figure 5: Uncoded use less ambulatory care, more acute careWhile they utilize less ambulatory care, uncoded patients use more acute care thancoded patients. Their rate of ER and inpatient utilization is 14% and 19% higherrespectively. Similarly, their length of stay per inpatient visit is 17% higher. This patternmakes sense given more uncoded patients are at-risk. Hence, a significant opportunityexists for practices to identify uncoded patients and engage them with more proactive,ambulatory care in order to reduce the amount of reactive, acute care.Strong clinical improvement after codingWhile it is clear that uncoded patients are relatively high-risk, and they access lessambulatory care, the question is then what happens after they get coded? Can codingenable a positive clinical impact? We next analyzed performance on condition-specificclinical measures before and after coding, for patients that became coded versus thosewho remained uncoded. The results were striking.Newly coded patients have high rates of clinical improvementAfter only 12 months post “go live” with population analytics, newly coded patientsshowed significantly higher rates of clinical improvement. Forty-seven percent of newlycoded diabetes patients improved on at least one clinical measure versus only 24% ofthose that remained uncoded (p-value .0001). This difference was also statisticallysignificant for hypertension and dyslipidemia patients. The impact may even be underestimated, as newly coded patients were not necessarily coded for the full 12 months.They were considered “newly coded” if they were coded at any time within 12 monthsof their group’s “go-live” access to population analytics.Optumwww.optum.comPage 5
White PaperAccurate coding: the foundation of accountable careCoding enables high rates of clinicalimprovement% of Patients with any clinical improvement*12 months post 0%0%DMHTNNewly codedDYSNever codedNote: Clinicalimprovementon at 1least1 clinicalmeasureforrelevantthe relevantcondition. 416K, Note: Clinicalimprovementon at leastclinicalmeasurefor thecondition.DM DMN N416K,HTNHTNN N889K,DYS N 889K,880K.codedpatients12could880K.codedwere notcodedforthemonths;couldcodedatt anytime880K NewlyNl DYSd Nd patientsti t Newlyt necessarilyil wered dnotf necessarilyth fullf ll 12 codedthfortheyththe fullld bebmonths;d dtheyti bewithinithi olive”datewithpopulationanalytics.P-value .0001.months of their group’s “go live” date with MinedShare. P-value .0001.4Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.Figure 6: Coding enables high rates of clinical improvement4Newly coded patients have high levels of clinical improvementIn addition to higher rates of clinical improvement, newly coded patients also exhibithigher levels of clinical improvement. Across conditions, newly coded patients showedmore improvement on relevant clinical measures than patients that remained uncoded(p-value .0001). In diabetes, for example, of the 47% of newly coded patients withimprovement in A1c, 28% saw a decrease of more than 1 point. In contrast, of the 18%of uncoded patients with A1c improvement, only 23% saw a decrease of over 1 point.This was also true for improvement in blood pressure, for both diabetes and hypertension patients. It also held for improvement in LDL for dyslipidemia patients. In this case,of patients with improvement in LDL, 23% of newly coded patients decreased by over40 points, whereas only 11% of those who remained uncoded (but still improved onLDL) saw similar improvement.Highest improvement seen in highest risk groupHence, coding enables high rates and levels of clinical improvement in a short timeperiod. Proper coding and diagnosis tends to drive patients toward proper care. In addition, there is a link between degree of improvement and how well controlled patientswere to begin with. After coding, those who improved the most were those whohad the most room for improvement. Sixty-two percent of patients in the group thatimproved the most (“high clinical improvement”) were at-risk at baseline. This classification requires improvement on 2 or more relevant clinical measures within 24 months. Incontrast, only 42% of those with moderate clinical improvement were at-risk, and only34% of those who worsened were at-risk. This means better coding helps identify highrisk patients and enable significant clinical impact quickly.Optumwww.optum.comPage 6
White PaperAccurate coding: the foundation of accountable careAfter coding, highest improvement inhighest risk groups% of Patients at risk at baseline*70%12 months before ‘go-live’62%60%50%42%40%34%30%20%10%0%Newly coded, HIGH clinicalHigh clinical improvementimprovementafter codingNewly coded, MODERATEModerate clinicalclinical improvementimprovement after codingNewly coded, clinicalClinical worseningworseningafter codingNote:improvementimprovement2 or moreclinical24requiresmonths.Note:High mprovementon 2 or moreonrelevantclinicalrelevantmeasureswithin measures24 months. withinModerateimprovementrequiresimprovementon 1 clinicalmeasure24clinicalmonths.Worsening worseon netor moreon 1Moderateclinical measurewithin24 months. Worsening worseon net within1 or moremeasures.MeasuresincludedA1c,1LDL,BP for DM. For valuesexceedingdefinedclinical measures. Measures included A1c, LDL, BP for DM. For HTN measured BP. For DYS measured LDL, HDL andthreshold. Data for 561K patients with DM, HTN, DYS. Limited to patients live 24 Months on MinedShareTRIG. “At-risk” means 25% or more of relevant clinical measures have values exceeding defined threshold. Datafor 561K patients with DM, HTN, DYS. Limited to patientslive 24 Months on population analytics.Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.66Figure 7: After coding, highest improvement in highest risk groupsUtilization is ‘right-sized’ to achieve the clinical improvementIf coding enables more clinical improvement, what specifically drove this improvement?Based on the analysis, utilization in both inpatient and outpatient settings spiked in thefirst 12 months. This occurs as patients become more integrated into the health systemor practice, and their care is ‘right-sized’ relative to their need. In contrast, utilizationdecreased significantly for uncoded patients. Their use of the health system was low tospikesand then begins to taperstartUtilizationwith, and it decreasedfurther.for newly coded patientsUtilization trends over timeOutpatient visitsInpatient visits0100.10Per patientpperp yeary0.0960.080.0750.0640.050.0430 030.0320.0210.010.00Per patient per year712 months pre go- 12 months postlivego-liveNewly coded24 months postgo-live012 months pre go- 12 months post go- 24 months post goliveliveliveAlways codedNever codedNote: Data based on 561K DM, HTN, DYS patients live for 24 months.Note: Data based on 561K DM, HTN, DYS patients live for 24 months.7Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.7Figure 8: Utilization spikes and then begins to taper for newly coded patientsOptumwww.optum.comPage 7
White PaperAccurate coding: the foundation of accountable careIn the second year, utilization begins to taper for newly coded patients. However, itremains high relative to patients that had always been coded. This makes sense givennewly coded patients tend to be relatively high-risk patients. Similarly, they also havea relatively high rate of comorbidities. Sixty-five percent of newly coded patients hadcomorbidities at baseline compared to 59% of those who were already coded. Sickerpatients with higher levels of risk demand higher levels of care. Coding helps providersfind those patients and deliver appropriate care.Improvement linked to successful ‘right-sizing’ of careAmongst the newly coded, those who improved the most were those whose utilizationincreased the most. Newly coded patients who improved on 2 or more relevant clinicalmeasures increased their rate of outpatient use by 0.9 visits per year on average. Incontrast, those who experienced the most clinical decline increased outpatient use byonly 0.2 visits.Hence, those who saw the highest levels of improvement were those who both had themost room for improvement, and whose utilization rose to meet their higher level ofmedical need. This has strong implications for accountable care. If practices can identifyhigh-risk patients, shift their care to ambulatory settings, and ‘right size’ their care successfully, clinical outcomes will improve.Population analytics enables practices to easily find uncodedpatientsPopulation analytics enables practices to easily find uncoded patients by quickly displaying chronic condition populations based on clinical and coded evidence. This can bedone at the physician and practice level, enabling practices to see trends and find at-riskpatients with minimal effort.Swift and significant reduction in uncoded patientsEarlier analysis indicated that 37% of patients with a major chronic condition wereuncoded at baseline. This rate decreased to 23% after 24 months for practices live withpopulation analytics. For a practice with 500K patients, this means moving from 83K tocodingimprovementcan be50K Highuncodedratespatients,ofa decreaseof nearly40%made quickly% of Patients with a major chronic condition that are uncoded*40%35%30%25%20%15%10%5%0%# uncodedd d(500K patientpractice):37%31%Pre go-live83K27%24%23%6 mo12 mo18 mo24 mo68K60K55K50K39% decreasein 24 months.Thirty-sevenpercentis basedon pre goNote: DecreaseDecrease seenonon566Kpatientslive forlive24 formonths.Thirty-sevenpercent is basedon pregolivefrom4M 4M activepatientsin 5 chroniclivedatadata fromactivepatientsin 5 chronicdiseasediseasecohorts.cohorts.Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.Figure 9: High rates of coding improvement can be made quicklyOptumwww.optum.com8Page 8
White PaperAccurate coding: the foundation of accountable carePractices were able to significantly improve coding in all of the major chronic conditions.For diabetes, the rate of uncoded patients was nearly cut in half, decreasing from 17%to 9% over 2 years. Significant reductions were also seen for hypertension and dyslipidemia, whose uncoded rates went from 27% to 19% and 32% to 18% respectively.Significant decreases in uncoded patientsacross conditions% Uncoded by conditionFor groups live 24 9%23%18%13%11%10%9%5%0%Diabetes% at go-live*CHFHTN% at 12 months post go-liveDYSCAD% at 24 months post go-live2Note: Includes 565K patients in Humedica’s clinical database that have been live for at least 24 months.Note: Includes 565K patients in Humedica’s clinical database that have been live for at least 24 months.9 Figure 10: Significant decreases in uncoded patients across conditionsConfidential property of Optum. Do not distribute or reproduce without express permission from Optum.9Of course, variation exists amongst practices in their operational focus and capabilities.Of the practices live with population analytics for 24 months, all practices decreasedtheir rate of uncoded patients. The largest improvement seen was a 23-percentagepoint decrease, from 52% to 28% uncoded. The lowest was 8-percentage points, for agroup that started with an uncoded rate of 35%. On average, practices decreased theirrate of uncoded patients by 14-percentage points over 24 months.How better coding supports accountable careAll of this highlights the importance of population analytics. A robust analytics platformthat brings together clinical and claims data makes it easy for practices to find uncodedpatients. Coding improvement helps providers prepare for accountable care. Better coding identifies patients that are often high-risk and helps ensure they receive the higherlevels of care they need. This will eventually rebalance care in favor of ambulatory overacute settings, leading to better outcomes and lower costs in the long run.Reference:Humedica MinedShare database of nearly 40 million treated patients.Optumwww.optum.comPage 9
White PaperAccurate coding: the foundation of accountable careJeremy Orr MD, MPHChief Medical Officer, Optum AnalyticsJeremy is the Chief Medical Officer of Optum Analytics, and isresponsible for driving clinical input into Humedica’s analytics.Leveraging his clinical expertise, Jeremy is leading the effort tobuild benefits for healthcare providers to deliver patient care,supporting product strategy, and leading a team of subjectmatter experts. Jeremy originally joined Humedica in 2012 asthe Physician Director of Provider Solutions. Prior to Humedica,Jeremy was the founder and provider of Frontier Family Medicine, a medical practice,in Colorado. He also was an Assistant Professor and Clinical Faculty at the Universityof Colorado School of Medicine, where he taught inpatient and outpatient practiceand was selected by Residents as Teacher of the Year in 2011. Jeremy has more than20 years of population health experience, including outcomes research, large dataset investigation, chronic disease management, and provider operations. He has leadimplementations of a variety of EMRs, served as physician lead for meaningful use, andwas twice named a “Top 100 Physician” by Kaiser Permanente. Jeremy received hisMD from the University at Buffalo and his MPH in Epidemiology and Biostatistics fromTulane University.Allen KamerChief Commercial Officer, Optum AnalyticsAllen is one of Humedica’s co-founders and presently serves asGeneral Manager & Vice President Provider Markets. Previouslyhe was Humedica’s Vice President Corporate Development &Marketing, responsible for the company’s partnerships, marketing, and new business opportunities. Prior to Humedica, hewas a Director at Leerink Swann, a leading health care investment bank, where he helped develop the business plan andraise capital to launch Humedica. With nearly 20 years of health care experience, Allenheld management positions at Biogen and Biogen Idec, including Director of DecisionSupport. In that role, he managed a team responsible for the company’s forecasting,market research, managed care analyses, sales force design and compensation plans forthe Neurology and Dermatology business units. Prior to joining Biogen, Allen co-founded MORPACE Pharma Group, a forecasting and consulting firm for the pharmaceuticalindustry. There he led the company’s integration of secondary data sources, demandforecasting, and the development of a global physician panel. Allen began his careeras a reporter for The Pink Sheet, covering pharmaceutical industry issues at the FDAand Capitol Hill, and also previously worked at Decision Resources managing multiplepublications. He received his bachelor’s degree from Brandeis University.1380 Soldiers Field RoadBoston, MA About OptumOptum is an information and technologyenabled health services company servingthe broad health care marketplace,including care providers, health plans, lifesciences companies and consumers andemploys more than 30,000 people worldwide. For more information about Optumand its products and services, please visitwww.optum.com.About HumedicaHumedica, an Optum company, is theforemost clinical intelligence companythat provides private cloud-based business solutions to the health care industry. Humedica’s sophisticated analyticsplatform transforms disparate clinical datainto actionable, real-world insights. Powered by the largest and most comprehensive clinical database, Humedica solutionsmove beyond claims data to offer a morecomplete, longitudinal view of the patientpopulation. Through its award-winningsolutions, Humedica, which is headquartered in Boston, empowers its partnersand customers to make confident, valuebased decisions about patient care in arapidly changing healthcare market.Page 10
Accurate coding: the foundation of accountable care White Paper Optum www.optum.com Page 4 Measure At Risk A1C 9 LDL 130 BP SYS 140 & DIAS 90 HDL 40 TRIG 200 Figure 3: Risk thresholds by clinical measure Based on this methodology, 36% of uncoded patients were at-risk. Eleven percent of them fell into the highest risk category versus only .
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